Project
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1011
- Precision: 0.8994
- Recall: 0.9226
- F1: 0.9109
- Accuracy: 0.9802
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0934 | 1.0 | 2614 | 0.0791 | 0.9112 | 0.8850 | 0.8979 | 0.9787 |
| 0.0433 | 2.0 | 5228 | 0.0849 | 0.8960 | 0.9187 | 0.9072 | 0.9802 |
| 0.019 | 3.0 | 7842 | 0.1011 | 0.8994 | 0.9226 | 0.9109 | 0.9802 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
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